Method for accurately aligning and correcting images in high dynamic range video and image processing

ABSTRACT

An alignment method for high dynamic resolution imaging searches and detects features in an image and parameterizing the features. Then differently exposed images are compared by comparing the features. Additionally the method detects features in an image, performs shape adaptive filtering using scaling pyramids, determines trajectories, calculates a range of characteristics for each unit in the image, and compares characteristics of paired units to select or eliminate the pair. Paired unit characterization selects unit pairs, selects unit pairs by statistical analysis, performs cluster analysis, calculates alignment parameters, and performs additional correction of the alignment parameters. The brightness channel is used for objects detection.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to digital imaging. More specifically, thepresent invention discloses a method for accurately aligning andcorrecting images in high dynamic range video and image processing.

Description of the Prior Art

High dynamic range imaging is used to reproduce a greater dynamic rangeof luminosity in imaging and photography. A conventional technique ofhigh dynamic range imaging includes utilizing special image sensors foroversampling. Another technique involves merging multiple images.

However, the special image sensors often encounter difficulty when usedin low light conditions which produces a non-optimal resultant image.Additionally, digital image encoding does not always offer a greatenough range of values to allow fine transitions which causesundesirable effects due to lossy compression.

Therefore, there is need for an efficient alignment method foraccurately aligning and correcting images in high dynamic range videoprocessing that produces superior high dynamic range video at a highframe rate.

SUMMARY OF THE INVENTION

To achieve these and other advantages and in order to overcome thedisadvantages of the conventional method in accordance with the purposeof the invention as embodied and broadly described herein, the presentinvention provides an efficient method for accurately aligning andcorrecting images in high dynamic range video and image processing.

When a High Dynamic Range (HDR) image is being created through mergingof a series of Low Dynamic Range (LDR) images, the LDR images areusually being taken with different exposure times (using exposurebracketing to cover some necessary dynamic range) or ISO values. Thisrequires taking images at different shot times, which can causemisalignment of the LDR images when the camera was moving or shakingduring shots (i.e. cameras without a tripod). For this situation specialalgorithms are required to align and correct the images before themerging into an HDR image.

Following is a description of the alignment method which is based onseparate objects detection and analysis.

The method comprises a step of searching for the features(characterizing elements) in an image, a step of parameterization ofthose features, and a step of comparison of differently exposed imagesby comparing the features.

The brightness channel is used for objects detection. Also, other colorchannels of the image can be used as a source of additional parametersfor objects recognition.

The brightness channel is represented in a logarithmic scale as itsdistribution on a discrete space of pixel coordinates. It is intendedthat the brightness channel is continuous (on the discrete space) overthe whole image, and its first and second spatial numerical derivatives(defined on the discrete space) exist and have finite values at any(x,y) point of the brightness channel.

Since the brightness channel is analyzed in a logarithmic scale on thebasis “2” (EV scale), black and white levels limitations are introducedfor all pixels in the image by the following limits:

-   -   Black level is limited as −32 EV;    -   White level is limited by a maximal possible value in the        initial image data representation and in the EV scale is equal        to 0.0 EV.

This means that there will be no values above 0.0 EV but all valuesbelow −32 EV should be set to −32 EV.

For the given algorithm, objects in the image are supposed to beconstructed from their smallest detectable parts—details. For objectsparametric representation the notions of trajectory, stream and unit areused. Trajectories are the trivial features characterizing an image.They compose streams, and streams compose units. Comparison of thedetails between two images will be performed through comparison of theirrelevant units' parameters.

Each unit is intended to have just one reference point (center), whichreflects a single extremum point of the brightness channel.

Any unit contains a sufficient set of parameters, which willcharacterize its relevant detail:

-   -   shape (including brightness distribution characteristics);    -   size;    -   position;    -   orientation; and    -   brightness distribution of its pixels etc.

For better accuracy and for wider recognition possibilities, somesmaller gradient-like components of units—streams—are introduced here.So, the units are intended to be nontrivial parts of the imageparameterization, and they can be separated into their smaller trivialparts. Any stream contains a sufficient set of parameters, whichcharacterizes its relevant trivial detail's part. Streams parametersconstruct the parameters of its' unit. A stream can belong to severalneighboring units (coupling the units' centers).

An image can also be represented as a set of objects, and any objectparameterization will be characterized as a unique combination(aggregation) of appropriate units, and these units—as uniquecombinations of their composing elements.

Since details in the image can be of different sizes and significance,and some of them can construct objects of different shapes and sizes,for performance and recognition improvement the objects detection andparameterization is performed through scaling pyramid. The pyramidconsists of scaling levels. Each level characterizes objects separationinto their smallest (for the current level) nontrivial parts. Thesesmall parts, in turn, can be separated into their smaller nontrivialparts on the lower pyramid level, and so on.

Aggregation of units of the given level into units of the next level isdefined through coupling elements of neighboring units.

These and other objectives of the present invention will become obviousto those of ordinary skill in the art after reading the followingdetailed description of preferred embodiments.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary, and are intended toprovide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the invention, and are incorporated in and constitute apart of this specification.

The drawings illustrate embodiments of the invention and, together withthe description, serve to explain the principles of the invention. Inthe drawings:

FIG. 1A is a flowchart illustrating a method for high dynamic resolutionvideo and image processing according to an embodiment of the presentinvention;

FIG. 1B is a flowchart illustrating a method for high dynamic resolutionvideo and image processing according to an embodiment of the presentinvention; and

FIG. 2 is a flowchart illustrating an alignment method of unitscomparison according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers are used in thedrawings and the description to refer to the same or like parts.

Refer to FIG. 1A, which is a flowchart illustrating an alignment methodfor high dynamic range imaging according to an embodiment of the presentinvention.

The method 10, begins in Step 11 by searching for the features(characterizing elements) in an image. Next, in Step 12 the methodcontinues with a step of parameterization of those features. And Step 12comprises a step of comparison of differently exposed images bycomparing the features.

Refer to FIG. 1B, which is a flowchart illustrating an alignment methodfor high dynamic range imaging according to an embodiment of the presentinvention.

The method 50 begins in Step 100, by detecting features in an image.

In order to find details in an image and get some parametricrepresentation of them (units), some spatial filtering procedures areapplied to the image. Filtering allows detection of as much usefulinformation as possible while rejecting spatially non-correlated data(as noise, for example). Parameters of the units are being found asresponses of characteristic filters on the image details.

During filtering, classical square-kernel image filtering is avoided,because of well known problems of such constantly distributedsymmetrically shaped FIR filters:

-   -   The filter should be spatially symmetric, so it always has        circular symmetry for its elements distribution. Thus, this        filter can give a response just on the details' size, and it is        not always aware of the shape. That's why, for arbitrary shaped        details analysis, filters of different sizes should be used.    -   Application of a fixed-pattern filter usually requires multiple        convolution operations, so, the bigger filter's aperture, the        bigger amount of multiplications are required for each pixel        position before integration. Since for different details' sizes        all set of convolutions with different apertures sizes are also        required (to analyze the whole spectrum of sizes), the amount of        calculations grows significantly.    -   Data losses take place because any shapes and sizes of        characterizing details proper apertures are not used.

In Step 200, shape adaptive filtering using scaling pyramids isperformed. For the given elements detection algorithm an arbitraryshaped filtering is introduced, which detects elements by fittingnoticeable details with the aid of adaptively shaped filters, whoseshapes (and characteristic sizes) are (in turn) defined by the detailsthemselves.

In the present invention the brightness channel of the image is treatedas a quasi-static potential phase field. Each detail of the image hasits own unique local potential distribution as a part of the field.

In Step 300, trajectories are determined. In order to detect andparameterize details, first, the potential phase field is being analyzedthrough quasi-dynamic behavior of an imaginary “charged probe particle”placed at any (x,y) point of this field. The particle behavior isdescribed here by means of a single unique trajectory formed by themotion of this particle along an instantaneous local gradient at thecurrent particle's (x,y)-position in the field.

Analysis is performed for both positive and negative charges of theparticle. The darker areas of the field have negative spatial charges,which attract positively charged particles, and vice-versa, bright areasof the field have positive spatial charges, which attracts negativelycharged particles. The darker and brighter areas of attraction havetheir appropriate points of attraction (or cycles) where averagedgradient is equal to zero. Any trajectory by both positive and negativeprobe particles placed subsequently at the same initial (x,y) coordinateon the phase field and then moved along an instantaneous local gradientof the field is analyzed. For a positive charged particle its point ofattraction will be denoted as (−) point (dark point), and for a negativecharged particle its point of attraction will be denoted as (+) point(bright point). Since the motion of positive and negative particlesstart from the same initial (x,y) position, this motion will construct atrajectory, which is similar to an “electrical current channel” between(+) and (−) points. That's why (+) and (−) points of the trajectory aredenoted as starting and ending points accordingly. Starting and endingpoints are considered potential values, and the brightness differencebetween them is considered as a potential difference.

For the spatial filtering just the geometry of this particle motion isanalyzed. Its velocity or acceleration can be used in further analysisas additional recognition parameters.

As described above, the brightness channel (as potential field) iscontinuous over the image and its spatial derivatives have finite valuesat any point, so any trajectory is definable on the field and, if thebrightness channel has any area with nonzero gradient values (which canbe treated as a “detail”), there is at least one non-zero sizedtrajectory, which has its attractive steady points (including points atinfinite distance).

In embodiments of the present invention there are several ways tocalculate local gradient, and some of them allow steady cycletrajectories. For steady cycles, their center points can be consideredas steady points (i.e. the trajectory's endings).

For some other methods the situation of indeterminateness can take placewhen there is more than one possible direction for the particle to movefrom the current position. In this case the trajectory can branchcreating several new trajectories which share some pixels while havingdifferent starting and/or ending points.

The absolute gradient value is not equal to zero at any point of thetrajectory, except for its starting and ending points, so the trajectoryis always continuous on the discrete space of the brightness channel.

Each finite trajectory, which has both steady points ((+) and (−))inside the image boundaries and has its length larger than some minimallength value, can be used to parameterize a detail.

Under the term «trajectory», a unique continuous channel with twoend-points ((+) and (−)) for motion of a “charged probe particle”, isused.

Trajectories are obtained through point-by-point motion in the directionof highest local gradient. Local gradient can be calculated for thecentral pixel in the 3×3 (or more generally n×n) pixels aperture.

In Step 400, a range of characteristics for each unit in an image iscalculated. Suppose all the possible trajectories have been built forthe given B channel (brightness channel of some image). The starting andending points of those trajectories become centers of units. If thereare two units which share one ore more trajectories then the set of allthose common trajectories is called a stream. Streams are elements whichconstruct unit's parameterization. A stream has its starting point andending point derived from its composing trajectories.

Thus, the Shape Adaptive Image Filtering (SAIF) is constructed as aprocedure, where elements are defined as streams formed by thetrajectories.

Unit is an area of the image which consists of a central pixel, in whichthe brightness function B(x,y) reaches an extremum, and of all thestreams of the image which have their starting or ending point in thatpixel. The units which correspond to local minimums of B function arecalled dark units. Otherwise they are called bright units.

For each unit in the image a range of characteristics is calculated. Inan embodiment of the present invention the implementation of thealgorithm comprises the following characteristics:

-   -   Either the unit is dark or bright;    -   The units' center brightness B(x_(c),y_(c));    -   Number of streams;    -   Luminance differences between the streams starting and ending        points;    -   Square area covered by the units pixels;    -   For each trajectory of the unit: its length, calculated, for        example, as a sum of distances between each two neighboring        pixels of the trajectory, so that for the pixels with the same x        or y coordinate the distance equals 1, and in other cases it        equals √        ;    -   For each stream: its length, calculated, for example, as root        mean square of the comprised trajectories;    -   For each stream: its «vector» as a directed line segment with        its starting and ending points coinciding with the starting and        ending points of the stream accordingly;    -   For each stream: its embroidery as the quotient obtained when        its length is divided by the length of its vector; and    -   For each two neighboring streams of the unit: the angle between        their vectors.

In other embodiments other characteristics are used as well.

The described set of characteristics can serve for the units comparisonbetween differently exposed images in order to find correspondingelements of the images. The specific property of the characteristics isthat they are invariant to any shift and rotation of one imagerelatively to another, which makes it possible to use them for imagesalignment to overcome problems in HDR image creation. If at least twopairs of units are found, the angle and shift for the alignment can beeasily calculated.

An example of a pyramid based on a classical filter with the Gausskernel is the so-called Gauss pyramid. The B channel is taken as itszero level. Given k'th level, the k+1 level is being formed asfollowing: the Gauss filter with constant radius r is being applied tothe pixels of the k-level image in increments of r+1. For example, the3×3 filter is being applied to every other pixel. The Gauss kernel isset by the function:

$\begin{matrix}{{f(x)} = {\frac{1}{\sigma \sqrt{\left( {2\; \pi} \right)}}\exp \frac{\left( {r - µ} \right)^{2}}{2\; \sigma^{2}}}} & (1)\end{matrix}$

In the current implementation the parameters are set to σ=1, μ=0.The resulting values form an image with

${{width}_{k + 1} = \frac{{width}_{k}}{{2\; r} + 1}},\mspace{14mu} {{height}_{k + 1} = \frac{{height}_{k}}{{2\; r} + 1}},$

which becomes the level number k+1.

Creation of new levels proceeds while the highest levels' width andheight are larger than some established width_(min), height_(min)values.

In another embodiment of the present invention, another version of thealgorithms' implementation uses the kind of pyramid in which the lowerlevel elements (units, streams, trajectories) are being taken as a basisfor the higher levels elements construction.

In this embodiment a pixel is considered as a trivial unit which onlyconsists of its central point and does not contain streams of non-zerolength, and the B channel of the original image is taken as the zerolevel of the pyramid.

The algorithm of trajectories construction described above is used forthe first-level elements construction. Therefore, in the defined termsthe imaginary particle, which draws the 1st-level trajectories, movesfrom one unit of the zero level to another sequentially by the lawderived from the relative brightness of the neighboring units. Thetrajectories of the k=2 . . . N levels should be constructedanalogously. Each of the (k+1)-level trajectories contains units of thek'th level as its joints and its starting and ending points.

The units which share streams are called neighboring units.

The trajectories construction process on the higher levels requires amethod of determining a direction of the imaginary particles movement,analogously to the gradient estimation on the zero level.

For example, the following way can be used: suppose the negativelycharged particle is set to a bright unit U_(O) ^(B), which obviously hasonly dark neighboring units U₁ ^(D) . . . U_(K) ^(D). Then the unitwhere the particle will move to on the next step is the brightest unitU_(S) ^(B), in the union of all the bright units neighboring to the U₁^(D) . . . U_(K) ^(D) units, except for U_(O) ^(B), and only in case ifU_(S) ^(B) is brighter than U_(O) ^(B) (in terms of average brightness).To find such a unit the function of a units' average brightnessestimation is needed.

That function can, for example, calculate average brightness of a unitas an average of all the pixels which compose the unit, or as an averageof all the streams' average brightness. Thus the particle jumps to U_(S)^(B), and the dark unit U_(l) ^(D), lϵ1 . . . K neighboring to U_(S)^(B) will be added to the current trajectory as a joint.

In case there are more than one possible choice of U_(S) ^(B) and/orU_(l) ^(D) the trajectory will branch.

The positively charged particle will move from a bright unit to thedarkest of its neighboring units.

In case when the starting unit is dark, the choice of the particlesmovement direction is performed symmetrically.

Once a trajectory has stopped at one of the k−1 levels units, thisunits' center becomes the center of the new k levels' unit. As the new klevel trajectory consists of several k−1 level trajectories linkedtogether, its length can be calculated as a sum of the lengths of thosetrajectories. Other elements' characteristics can also be calculatedanalogically to the previous level.

Each level of a pyramid's units can be constructed and used to comparethe corresponding levels of different pictures. Thus on each level anangle and shift for alignment can be found.

The higher the level indicates the rougher this estimation is. On theother hand, the basic level is usually noisy, which can detrimentallyaffect the precision. Therefore all levels should be taken into accountin order to perform the alignment with the best possible precision.

Any pair of units itself provides its values of angle and shift of oneimage relatively to another. Suppose the units U₁ and U₂ are coupled.Then the distance between their centers sets the shift, and the anglebetween their corresponding streams sets the rotation angle. When allpossible pairs of units are found on the current level, the specialalgorithm is needed to average the angles and shifts they set. Supposethe algorithm has returned the values (dx_(k), dy_(k)) of shift andα_(k) for rotation angle after the averaging process on level k. Thenafter going down to the k−1 level, for each two units which are supposedto be coupled the values of the shift and the rotation angle they setmust not differ much from the (dx_(k), dy_(k)) and α_(k) values.Otherwise the pair is to be eliminated.

In Step 500, the characteristics of paired units are compared to selector eliminate the pair. An effective algorithm of units comparison posesone of the key problems for alignment.

The alignment method of units comparison of the present inventioncomprises the following steps as illustrated in FIG. 2.

Step 1. Primary Selection of Pairs

In Step 1 a large amount of pairs is usually formed, most of which canbe eliminated in the further steps.

The comparison in this step is based on estimation of difference of themain unit's characteristics. Each characteristic can have its individualfunction for difference estimation.

Suppose the images I₁ and I₂ are being compared. Each unit u_(1 i) of I₁is being compared to each u_(2 j) of I₂, if both u_(1 i) and u_(2 j) aredark or both are bright.

The first check uses such parameters as square area and number ofstreams.

If the parameters differ by a large value then the pair is beingeliminated.

Suppose S₁₁ . . . S_(1N) ₁ are all the streams which belong to u_(1 i)and S₂₁ . . . S_(2 N) ₂ —are those of u_(2 j).

For each of u_(1i) and u_(2 j) a «characterizing» stream is chosen tostart the comparison with. For example, the streams S_(1 n) ₁ , S_(2 n)₂ with the maximal embroidery can be taken: E(S_(kn) _(k) )=max_(n)E(S_(kn)).

The centers of the units are being aligned, and the u_(2 j) is beingrotated relatively to the u_(1i) until the chosen streams coincide.

For each stream S_(kn) the angle α_(kn) is introduced between it and thecharacterizing stream of the unit when moving clockwise.

The most interesting parameters are selected for streams comparison. Inan embodiment of the present invention streams embroidery, length andvectors' length are used.

Then the iteration over streams is performed, clockwise orcounterclockwise. Beginning with the first coinciding streams S_(1 n),and S_(2n) ₂ , each pair of streams which have an angle|(α_(1n)−α_(2m)|)| between them smaller than a predetermined ϵ_(α), thevalues characterizing the agreement by the chosen parameters are beingcalculated and stored into memory. Those values can be the ratio ofthose parameters provided the denominator differs from 0.

If ϵ_(α)-neighborhood of a stream of one of the units does not containany stream of another unit, then the value which corresponds to theminimal possible likeness by each of the parameters is stored. After allthe iterations over the streams are completed, the stored values areaveraged and the result reflects the likeness of the units.

There can be different ways of choosing the first characterizing streamsS_(1 n) ₁ , S_(2 n) ₂ for comparison. The described algorithm of thepresent invention can be repeated several times with different choice ofS_(1 n) ₁ , S_(2 n) ₂ and the variant with the maximal resulting valueof likeness can be used to decide whether the units should be coupled ornot.

Each pair u_(1 i), u_(2 j) which has passed the stages described abovecan then be checked with use of some additional parameters, for exampledifferences of brightness between the endings of the coinciding streamsshould not differ much.

Step 2. Selection with Statistical Approach.

The second step works with the set of pairs p₁ . . . p_(N) collected inStep 1 and considers random variables: the angle α, and the shifts dx

d along the two axis of an image. Their mean values correspond to thetarget parameters of alignment. The α_(i), dx_(i)

dy_(i), provided by a pair p_(i), can be thought of as the i-thmeasurement of those variables.

Thus there are N measurements available. In practice, N is usually largeenough to estimate the target variables. By the N measurements thedistribution graphs of the random variables can be constructed. Thatgraph can be plotted as a histogram with values of the random variablesalong the abscises axis and the amount of the measurements, which fellinto each interval, along the ordinates axis. If a histogram reaches adistinctive maximum at some point then that maximum reflects the meanvalue of the corresponding random variable. Thus in this step all thepairs which fall far from that maximum are eliminated.

Actually each of the three histograms will set a different set ofselected pairs, therefore the intersection of those sets can be taken asa resulting set of pairs. In practice, this set is usually not empty andlarge enough to perform alignment, provided the significant number ofpairs has been gained in Step 1.

Step 3. Cluster Analysis

In cases where the number of the unit pairs formed Step 1 is not large(for example about 30 or less), cluster analysis can be performedinstead of (or along with) statistical analysis. Units in pairs are putin order (the first is the unit of the key image and the second is ofthe image which is to be aligned). The process of alignment when centersof the units of the second picture are being set to the centers of theunits of the first picture can be thought of as movement. The goal is tofind the maximal subset in the set of all pairs such that all the unitsof this subset which go second in their pair move together as a solidbody. All the pairs beyond this subset are eliminated from the alignmentprocess. In order to find the described subset different approaches canbe used. For example all the centers of the potential subset can belinked with line segments. Thus it can be seen if the lengths of thosesegments and the angles between them are not changed during the movement(or the changes are minimal). Using these criteria the pairs can beeliminated by ones and it can be estimated how much the lengths and theangles change in the process of movement of the remaining pairs. Thusthe pair exclusion of which leads to the best result can be found andeliminated and then the procedure can be repeated with the remainingpairs.

Step 4. Alignment Parameters (Angle of Rotation and Shift) Calculation.

The pairs selected in the previous steps are being used for the angleand shift approximation.

The angles between the corresponding streams of the coupled units are nolonger used for the target angle estimation. The coordinates (x,y) ofthe units centers are of interest, and an algorithm is used whichcalculates such angle and shift of an image alignment (not takingpossible optical distortions into account) which provide the least meansquare variation of all the coupled units centers coordinates.

In this way the first approximation of the alignment parameters isobtained.

Step 5. Additional Correction of the Parameters Estimation.

Some additional correction of the angle and shift values is oftenrequired. For that purpose the pairs of units which had been eliminatedin Step 2 are brought back into consideration. Then those which setangle and shift close enough to those later obtained are added to thefinal set of pairs for alignment. Then the calculations are repeatedwith participation of the retrieved pairs centers.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the present inventionwithout departing from the scope or spirit of the invention. In view ofthe foregoing, it is intended that the present invention covermodifications and variations of this invention provided they fall withinthe scope of the invention and its equivalent.

1. An alignment method for high dynamic resolution imaging comprising:applying spatial filtering to an image to detect details while rejectingspatially non-correlated data; performing shape adaptive filtering usingscaling pyramids; detecting elements by fitting noticeable details withaid of adaptively shaped filters whose shapes and characteristic sizesare defined by the noticeable details; utilizing brightness channel ofthe image as a quasi-static potential phase field; wherein each detailof the image has its own unique local potential distribution as a partof the quasi-static potential phase field; determining trajectories of aparticle's motion along an instantaneous local gradient at theparticle's position in a field; performing analysis for both positiveand negative charges of the particle; wherein darker areas of the fieldhave negative spatial charges which attract positively chargedparticles, and brighter areas of the field have positive spatial chargeswhich attract negatively charged particles; analyzing trajectory of bothpositive and negative probe particles at a same initial coordinate inthe field and then moving along an instantaneous local gradient of thefield; calculating a range of characteristics for each unit in theimage; and comparing characteristics of paired units to select oreliminate the paired units.
 2. (canceled)
 3. The alignment method forhigh dynamic resolution imaging of claim 1, where black level of thebrightness channel is limited to −32 EV and white level is limited by amaximal possible value in initial image data representation in an EVscale equal to 0.0 EV.
 4. The alignment method for high dynamicresolution imaging of claim 1, where the step of calculating the rangeof characteristics comprises parameterizing trajectory, streams, andunits.
 5. (canceled)
 6. An alignment method for high dynamic resolutionimaging comprising: applying spatial filtering to an image to detectdetails while rejecting spatially non-correlated data; performing shapeadaptive filtering using scaling pyramids; detecting elements by fittingnoticeable details with aid of adaptively shaped filters whose shapesand characteristic sizes are defined by the noticeable details;utilizing brightness channel of the image as a quasi-static potentialphase field; wherein each detail of the image has its own unique localpotential distribution as a part of the quasi-static potential phasefield; determining trajectories of a particle's motion along aninstantaneous local gradient at the particle's position in a field;performing analysis for both positive and negative charges of theparticle; wherein darker areas of the field have negative spatialcharges which attract positively charged particles, and brighter areasof the field have positive spatial charges which attract negativelycharged particles; analyzing trajectory of both positive and negativeprobe particles at a same initial coordinate in the field and thenmoving along an instantaneous local gradient of the field; calculating arange of characteristics for each unit in the image; wherein if twounits share one or more trajectories a set of all common trajectoriescomprises a stream which constructs parameterization of the units; andwherein calculating the range of characteristics comprises: determiningwhether the unit is dark or bright; determining the units' centerbrightness; determining number of streams; determining luminancedifferences between the stream's starting and ending points; determiningsquare area covered by the unit's pixels; calculating length of eachtrajectory of the unit as a sum of distances between each twoneighboring pixels of the trajectory, so that for pixels with same x ory coordinate distance equals 1, and in other cases it equals square rootof 2; and calculating length of each stream as root mean square of thetrajectories; and comparing characteristics of paired units to select oreliminate the paired units.
 7. The alignment method for high dynamicresolution imaging of claim 6, where the step of comparingcharacteristics of paired units comprises: selecting unit pairs;selecting unit pairs by statistical analysis; performing clusteranalysis; calculating alignment parameters; and performing additionalcorrection of the alignment parameters.
 8. (canceled)
 9. The alignmentmethod for high dynamic resolution imaging of claim 6, where black levelof the brightness channel is limited to −32 EV and white level islimited by a maximal possible value in initial image data representationin an EV scale equal to 0.0 EV.
 10. The alignment method for highdynamic resolution imaging of claim 6, where in the step of determiningtrajectories steady cycle trajectories are allowed and center points areconsidered as steady points.
 11. The alignment method for high dynamicresolution imaging of claim 6, where in the step of determiningtrajectories there are more than one possible direction for a particleto move from a current position.
 12. An alignment method for highdynamic resolution imaging comprising: applying spatial filtering to animage to detect details while rejecting spatially non-correlated data;performing shape adaptive filtering using scaling pyramids; detectingelements by fitting noticeable details with aid of adaptively shapedfilters whose shapes and characteristic sizes are defined by thenoticeable details; utilizing brightness channel of the image as aquasi-static potential phase field; wherein each detail of the image hasits own unique local potential distribution as a part of thequasi-static potential phase field; determining trajectories of aparticle's motion along an instantaneous local gradient at theparticle's position in a field; performing analysis for both positiveand negative charges of the particle; wherein darker areas of the fieldhave negative spatial charges which attract positively chargedparticles, and brighter areas of the field have positive spatial chargeswhich attract negatively charged particles; analyzing trajectory of bothpositive and negative probe particles at a same initial coordinate inthe field and then moving along an instantaneous local gradient of thefield; calculating a range of characteristics for each unit in theimage; wherein if two units share one or more trajectories a set of allcommon trajectories comprises a stream which constructs parameterizationof the units; and wherein calculating the range of characteristicscomprises: determining whether the unit is dark or bright; determiningthe units' center brightness; determining number of streams; determiningluminance differences between the stream's starting and ending points;determining square area covered by the unit's pixels; calculating lengthof each trajectory of the unit as a sum of distances between each twoneighboring pixels of the trajectory, so that for pixels with same x ory coordinate distance equals 1, and in other cases it equals square rootof 2; and calculating length of each stream as root mean square of thetrajectories; and comparing characteristics of paired units to select oreliminate the paired units; performing cluster analysis on selectedpared units; calculating alignment parameters; and performing additionalcorrection of the alignment parameters.